code: def test_kurtosisses(sparse_data_transformed):
      fixture = [
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          -1.1005836114255763,
          -1.1786325509475744,
          -1.23879983823279,
          1.3934382644137013,
          -0.9768209837948336,
          -1.7937072296512784,
      ]
      (
          X_transformed,
          y,
          categorical_transformed,
      ) = sparse_data_transformed
      mf = meta_features.helper_functions["Kurtosisses"](
          X_transformed, y, logging.getLogger("Meta")
      )
      np.testing.assert_allclose(mf.value, fixture)
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 16 for calling a global function with example usage: test_kurtosisses(sparse_data_transformed) without return types